1. Field of Invention
The present invention relates to an image processing technology, and more particularly to an apparatus and a method of recognizing the feature pixels of an image.
2. Related Art
Virtual reality is a quite convenient visual communication tool. The virtual reality means establishing a new interface enabling users feel in a space of a computer simulation environment and move therein at will, so that the users may be personally on the scene.
Generally speaking, the scene of the virtual reality may be generated through two methods, one is to use three-dimensional (3D) objects to construct illusive scenes, and the other is panoramic image or panorama which is suitable for introducing a real scene. The panoramas mean the photos with an omni-directional scene or view of 360°. In brief, panoramic images or panoramas are made of stitching multiple images into single image with an omni-directional vista. For example, multiple images are shot in a center toward the surrounding with a fixed rotation angle and then stitched one by one using an image stitching technology, and furthermore, the two continuous images of the same scene are stitched seamlessly, thereby obtaining all-round panoramas.
Conventionally, feature pixels found in each image are used as reference to stitch images, and then the corresponding boundaries of stitched image are faded, thereby obtaining a seamless panorama.
In order to obtain better performance, multiple difference of Gaussian (DOG) blurring operations to find relative extreme values of images is often used to recognize objects or composite panoramas, and pixels with the relative extreme values found by multiple difference of Gaussian (DOG) blurring are considered as representative feature pixels of an image.
However, if a high resolution image (for example, more than 1 million pixels) is processed by using multiple difference of Gaussian (DOG) blurring operations, the operation process is usually too complicated and time-consuming. Especially, it is quite difficult to implement the difference of Gaussian (DOG) blurring function in an embedded system to deal with a high resolution image.